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Nicotine binds to nicotinic acetylcholine receptors (nAChR), which can exist as many different subtypes. The α4β2 nAChR is the most prevalent subtype in the brain and possesses the most evidence linking it to nicotine seeking behavior. Herein we report the use of mixture based combinatorial libraries for the rapid discovery of a series of α4β2 nAChR selective compounds. Further chemistry optimization provided compound 301, which was characterized as a selective α4β2 nAChR antagonist. This compound displayed no agonist activity but blocked nicotine-induced depolarization of HEK cells with an IC50 of approximately 430 nM. 301 demonstrated nearly 500-fold selectivity for binding and 40-fold functional selectivity for α4β2 over α3β4 nAChR. In total over 5 million compounds were assessed through the use of just 170 samples in order to identify a series of structural analogues suitable for future optimization toward the goal of developing clinically relevant smoking cessation medications.

Scaffold Ranking and Positional Scanning Utilized in the Discovery of nAChR-Selective Compounds Suitable for Optimization Studies